Overview

Dataset statistics

Number of variables13
Number of observations16247
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory104.0 B

Variable types

Categorical1
Numeric12

Warnings

gene has a high cardinality: 16247 distinct values High cardinality
C42_1 is highly correlated with C42_2 and 10 other fieldsHigh correlation
C42_2 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42_3 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42B_1 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42B_2 is highly correlated with C42_1 and 5 other fieldsHigh correlation
C42B_3 is highly correlated with C42_1 and 10 other fieldsHigh correlation
LNCAP_1 is highly correlated with C42_1 and 10 other fieldsHigh correlation
LNCAP_2 is highly correlated with C42_1 and 9 other fieldsHigh correlation
LNCAP_3 is highly correlated with C42_1 and 10 other fieldsHigh correlation
MR49F_1 is highly correlated with C42_1 and 10 other fieldsHigh correlation
MR49F_2 is highly correlated with C42_1 and 9 other fieldsHigh correlation
MR49F_3 is highly correlated with C42_1 and 10 other fieldsHigh correlation
C42_1 is highly correlated with C42_2 and 10 other fieldsHigh correlation
C42_2 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42_3 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42B_1 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42B_2 is highly correlated with C42_1 and 5 other fieldsHigh correlation
C42B_3 is highly correlated with C42_1 and 10 other fieldsHigh correlation
LNCAP_1 is highly correlated with C42_1 and 10 other fieldsHigh correlation
LNCAP_2 is highly correlated with C42_1 and 9 other fieldsHigh correlation
LNCAP_3 is highly correlated with C42_1 and 10 other fieldsHigh correlation
MR49F_1 is highly correlated with C42_1 and 10 other fieldsHigh correlation
MR49F_2 is highly correlated with C42_1 and 9 other fieldsHigh correlation
MR49F_3 is highly correlated with C42_1 and 10 other fieldsHigh correlation
C42_1 is highly correlated with C42_2 and 8 other fieldsHigh correlation
C42_2 is highly correlated with C42_1 and 1 other fieldsHigh correlation
C42_3 is highly correlated with C42_1 and 3 other fieldsHigh correlation
C42B_1 is highly correlated with C42_1 and 3 other fieldsHigh correlation
C42B_3 is highly correlated with C42_1 and 5 other fieldsHigh correlation
LNCAP_1 is highly correlated with C42_1 and 4 other fieldsHigh correlation
LNCAP_2 is highly correlated with C42_1 and 7 other fieldsHigh correlation
MR49F_1 is highly correlated with C42_1 and 6 other fieldsHigh correlation
MR49F_2 is highly correlated with C42_1 and 5 other fieldsHigh correlation
MR49F_3 is highly correlated with C42_1 and 4 other fieldsHigh correlation
C42B_2 is highly correlated with C42B_1 and 10 other fieldsHigh correlation
C42B_1 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
MR49F_3 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
C42_1 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
MR49F_2 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
LNCAP_2 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
LNCAP_1 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
LNCAP_3 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
C42B_3 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
C42_3 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
MR49F_1 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
C42_2 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
gene is uniformly distributed Uniform
gene has unique values Unique
C42_2 has 211 (1.3%) zeros Zeros
C42_3 has 234 (1.4%) zeros Zeros
C42B_1 has 187 (1.2%) zeros Zeros
C42B_2 has 195 (1.2%) zeros Zeros
C42B_3 has 181 (1.1%) zeros Zeros
LNCAP_1 has 318 (2.0%) zeros Zeros
LNCAP_2 has 349 (2.1%) zeros Zeros
LNCAP_3 has 369 (2.3%) zeros Zeros
MR49F_1 has 347 (2.1%) zeros Zeros
MR49F_2 has 391 (2.4%) zeros Zeros
MR49F_3 has 406 (2.5%) zeros Zeros

Reproduction

Analysis started2021-09-03 02:25:04.080821
Analysis finished2021-09-03 02:25:49.425536
Duration45.34 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

gene
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct16247
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size127.1 KiB
ENSG00000000003
 
1
ENSG00000183617
 
1
ENSG00000183506
 
1
ENSG00000183508
 
1
ENSG00000183513
 
1
Other values (16242)
16242 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters243705
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16247 ?
Unique (%)100.0%

Sample

1st rowENSG00000000003
2nd rowENSG00000000419
3rd rowENSG00000000457
4th rowENSG00000000460
5th rowENSG00000001036

Common Values

ValueCountFrequency (%)
ENSG000000000031
 
< 0.1%
ENSG000001836171
 
< 0.1%
ENSG000001835061
 
< 0.1%
ENSG000001835081
 
< 0.1%
ENSG000001835131
 
< 0.1%
ENSG000001835201
 
< 0.1%
ENSG000001835271
 
< 0.1%
ENSG000001835301
 
< 0.1%
ENSG000001835701
 
< 0.1%
ENSG000001835761
 
< 0.1%
Other values (16237)16237
99.9%

Length

2021-09-02T22:25:49.701292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ensg000000000031
 
< 0.1%
ensg000001836171
 
< 0.1%
ensg000001835061
 
< 0.1%
ensg000001835081
 
< 0.1%
ensg000001835131
 
< 0.1%
ensg000001835201
 
< 0.1%
ensg000001835271
 
< 0.1%
ensg000001835301
 
< 0.1%
ensg000001835701
 
< 0.1%
ensg000001835761
 
< 0.1%
Other values (16237)16237
99.9%

Most occurring characters

ValueCountFrequency (%)
091209
37.4%
118255
 
7.5%
E16247
 
6.7%
N16247
 
6.7%
S16247
 
6.7%
G16247
 
6.7%
211778
 
4.8%
68946
 
3.7%
38563
 
3.5%
78546
 
3.5%
Other values (4)31420
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number178717
73.3%
Uppercase Letter64988
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
091209
51.0%
118255
 
10.2%
211778
 
6.6%
68946
 
5.0%
38563
 
4.8%
78546
 
4.8%
48236
 
4.6%
58044
 
4.5%
88020
 
4.5%
97120
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
E16247
25.0%
N16247
25.0%
S16247
25.0%
G16247
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common178717
73.3%
Latin64988
 
26.7%

Most frequent character per script

Common
ValueCountFrequency (%)
091209
51.0%
118255
 
10.2%
211778
 
6.6%
68946
 
5.0%
38563
 
4.8%
78546
 
4.8%
48236
 
4.6%
58044
 
4.5%
88020
 
4.5%
97120
 
4.0%
Latin
ValueCountFrequency (%)
E16247
25.0%
N16247
25.0%
S16247
25.0%
G16247
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII243705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
091209
37.4%
118255
 
7.5%
E16247
 
6.7%
N16247
 
6.7%
S16247
 
6.7%
G16247
 
6.7%
211778
 
4.8%
68946
 
3.7%
38563
 
3.5%
78546
 
3.5%
Other values (4)31420
 
12.9%

C42_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16097
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.873633301
Minimum0
Maximum9.82417682
Zeros82
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size127.1 KiB
2021-09-02T22:25:49.841923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1193089608
Q10.6984957421
median1.75935523
Q32.810570692
95-th percentile4.185666553
Maximum9.82417682
Range9.82417682
Interquartile range (IQR)2.112074949

Descriptive statistics

Standard deviation1.319332383
Coefficient of variation (CV)0.7041572021
Kurtosis-0.192118322
Mean1.873633301
Median Absolute Deviation (MAD)1.057375917
Skewness0.5500007249
Sum30440.92024
Variance1.740637937
MonotonicityNot monotonic
2021-09-02T22:25:49.999049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
082
 
0.5%
1.737992214
 
< 0.1%
3.690111163
 
< 0.1%
0.10922800873
 
< 0.1%
1.9253276013
 
< 0.1%
0.21786902462
 
< 0.1%
0.0078393894352
 
< 0.1%
2.6988413292
 
< 0.1%
1.7733045522
 
< 0.1%
2.5133256432
 
< 0.1%
Other values (16087)16142
99.4%
ValueCountFrequency (%)
082
0.5%
0.0013643493811
 
< 0.1%
0.0018533550431
 
< 0.1%
0.0018837532841
 
< 0.1%
0.0019569151231
 
< 0.1%
0.0020940374441
 
< 0.1%
0.0021473925961
 
< 0.1%
0.0021753048751
 
< 0.1%
0.0021913779961
 
< 0.1%
0.0023698054021
 
< 0.1%
ValueCountFrequency (%)
9.824176821
< 0.1%
8.1090563871
< 0.1%
7.8217955071
< 0.1%
7.6449370171
< 0.1%
7.4000600921
< 0.1%
7.3587506791
< 0.1%
7.090719491
< 0.1%
7.0292786381
< 0.1%
6.8248485571
< 0.1%
6.7795253881
< 0.1%

C42_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14899
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.470975692
Minimum0
Maximum10.47408629
Zeros211
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size127.1 KiB
2021-09-02T22:25:50.154770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.399655399
Q11.812863228
median3.324787279
Q35.001315209
95-th percentile6.929211369
Maximum10.47408629
Range10.47408629
Interquartile range (IQR)3.188451981

Descriptive statistics

Standard deviation2.029188935
Coefficient of variation (CV)0.5846162911
Kurtosis-0.785638385
Mean3.470975692
Median Absolute Deviation (MAD)1.590965869
Skewness0.2547675167
Sum56392.94206
Variance4.117607735
MonotonicityNot monotonic
2021-09-02T22:25:50.322055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0211
 
1.3%
4.8468173811
 
0.1%
3.093573247
 
< 0.1%
1.4251212897
 
< 0.1%
3.7637896827
 
< 0.1%
2.7105248187
 
< 0.1%
2.3895502977
 
< 0.1%
2.4075538387
 
< 0.1%
2.226230157
 
< 0.1%
5.7583849766
 
< 0.1%
Other values (14889)15970
98.3%
ValueCountFrequency (%)
0211
1.3%
0.010854361371
 
< 0.1%
0.012279745411
 
< 0.1%
0.015774062731
 
< 0.1%
0.01683564171
 
< 0.1%
0.018536424951
 
< 0.1%
0.018683087421
 
< 0.1%
0.019249606681
 
< 0.1%
0.024337632921
 
< 0.1%
0.025146683651
 
< 0.1%
ValueCountFrequency (%)
10.474086291
< 0.1%
9.7764509561
< 0.1%
9.734245491
< 0.1%
9.709803361
< 0.1%
9.5244912421
< 0.1%
9.5049562591
< 0.1%
9.4394600881
< 0.1%
9.373939661
< 0.1%
9.3415017561
< 0.1%
9.3131474641
< 0.1%

C42_3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14884
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.465547674
Minimum0
Maximum10.70029066
Zeros234
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size127.1 KiB
2021-09-02T22:25:50.472656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.423906818
Q12.031656036
median3.542020826
Q34.823072382
95-th percentile6.412240688
Maximum10.70029066
Range10.70029066
Interquartile range (IQR)2.791416346

Descriptive statistics

Standard deviation1.83756014
Coefficient of variation (CV)0.530236578
Kurtosis-0.6690717012
Mean3.465547674
Median Absolute Deviation (MAD)1.384923715
Skewness0.04434617256
Sum56304.75306
Variance3.376627267
MonotonicityNot monotonic
2021-09-02T22:25:50.627529image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0234
 
1.4%
4.95135984913
 
0.1%
3.19435818310
 
0.1%
4.265261559
 
0.1%
2.3377403178
 
< 0.1%
2.7104943538
 
< 0.1%
3.8667959848
 
< 0.1%
5.6409638688
 
< 0.1%
1.7647725488
 
< 0.1%
1.4683519127
 
< 0.1%
Other values (14874)15934
98.1%
ValueCountFrequency (%)
0234
1.4%
0.0072149244841
 
< 0.1%
0.018291324941
 
< 0.1%
0.022405240361
 
< 0.1%
0.025298713951
 
< 0.1%
0.027973513391
 
< 0.1%
0.028392251091
 
< 0.1%
0.029256297091
 
< 0.1%
0.031273871371
 
< 0.1%
0.034179318911
 
< 0.1%
ValueCountFrequency (%)
10.700290661
< 0.1%
10.288700411
< 0.1%
9.7562873481
< 0.1%
9.3685925481
< 0.1%
9.2821114921
< 0.1%
9.0552515751
< 0.1%
9.0346282771
< 0.1%
9.024255021
< 0.1%
8.9596456881
< 0.1%
8.9148858141
< 0.1%

C42B_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15613
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.918773055
Minimum0
Maximum10.32358267
Zeros187
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size127.1 KiB
2021-09-02T22:25:50.780174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1447911429
Q11.009657556
median2.788548116
Q34.512616774
95-th percentile6.450719129
Maximum10.32358267
Range10.32358267
Interquartile range (IQR)3.502959218

Descriptive statistics

Standard deviation2.062234389
Coefficient of variation (CV)0.7065415331
Kurtosis-0.8495525939
Mean2.918773055
Median Absolute Deviation (MAD)1.754316903
Skewness0.3446148066
Sum47421.30583
Variance4.252810676
MonotonicityNot monotonic
2021-09-02T22:25:50.927964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0187
 
1.2%
3.6753219529
 
0.1%
4.0723040998
 
< 0.1%
3.1812493737
 
< 0.1%
2.6261496085
 
< 0.1%
2.5287965615
 
< 0.1%
3.0072002515
 
< 0.1%
1.4154862535
 
< 0.1%
5.8497634025
 
< 0.1%
3.7902853225
 
< 0.1%
Other values (15603)16006
98.5%
ValueCountFrequency (%)
0187
1.2%
0.0059937805671
 
< 0.1%
0.0082044405781
 
< 0.1%
0.0088426357141
 
< 0.1%
0.009893515081
 
< 0.1%
0.010356621571
 
< 0.1%
0.010798102651
 
< 0.1%
0.012634348421
 
< 0.1%
0.012778469961
 
< 0.1%
0.013154331191
 
< 0.1%
ValueCountFrequency (%)
10.323582671
< 0.1%
9.456389831
< 0.1%
9.3657994341
< 0.1%
9.2837426511
< 0.1%
9.0886174381
< 0.1%
9.0741484431
< 0.1%
9.0135504271
< 0.1%
9.0113711211
< 0.1%
8.9167868981
< 0.1%
8.8695497471
< 0.1%

C42B_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14931
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.563954325
Minimum0
Maximum11.40843506
Zeros195
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size127.1 KiB
2021-09-02T22:25:51.069668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2283601025
Q11.388233287
median2.466024948
Q33.494395979
95-th percentile5.472677812
Maximum11.40843506
Range11.40843506
Interquartile range (IQR)2.106162692

Descriptive statistics

Standard deviation1.587348481
Coefficient of variation (CV)0.6191017
Kurtosis0.5509968324
Mean2.563954325
Median Absolute Deviation (MAD)1.051976127
Skewness0.6497734073
Sum41656.56592
Variance2.519675201
MonotonicityNot monotonic
2021-09-02T22:25:51.237250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0195
 
1.2%
2.88514957720
 
0.1%
1.95131621217
 
0.1%
2.47387105114
 
0.1%
1.22822960514
 
0.1%
3.11508460414
 
0.1%
2.65910354113
 
0.1%
1.7630780513
 
0.1%
2.36656519512
 
0.1%
2.57077006312
 
0.1%
Other values (14921)15923
98.0%
ValueCountFrequency (%)
0195
1.2%
0.0075410332411
 
< 0.1%
0.0075799628241
 
< 0.1%
0.0091949444421
 
< 0.1%
0.011370810751
 
< 0.1%
0.012835884821
 
< 0.1%
0.012969882811
 
< 0.1%
0.013019544171
 
< 0.1%
0.01345893911
 
< 0.1%
0.014165727151
 
< 0.1%
ValueCountFrequency (%)
11.408435061
< 0.1%
11.32430661
< 0.1%
10.988411911
< 0.1%
10.929503291
< 0.1%
10.653322951
< 0.1%
10.131928881
< 0.1%
9.9847203851
< 0.1%
9.6593431061
< 0.1%
9.5806733841
< 0.1%
9.3632002681
< 0.1%

C42B_3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15151
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.343280361
Minimum0
Maximum10.71824787
Zeros181
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size127.1 KiB
2021-09-02T22:25:51.382138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4542662817
Q11.885719111
median3.406084391
Q34.672537658
95-th percentile6.301911618
Maximum10.71824787
Range10.71824787
Interquartile range (IQR)2.786818548

Descriptive statistics

Standard deviation1.806866958
Coefficient of variation (CV)0.5404473339
Kurtosis-0.6346609609
Mean3.343280361
Median Absolute Deviation (MAD)1.380147917
Skewness0.1166613342
Sum54318.27603
Variance3.264768203
MonotonicityNot monotonic
2021-09-02T22:25:51.841164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0181
 
1.1%
3.36818963210
 
0.1%
2.08025158310
 
0.1%
4.7283053398
 
< 0.1%
3.1536221888
 
< 0.1%
1.3937243087
 
< 0.1%
1.5047974667
 
< 0.1%
1.8878551546
 
< 0.1%
2.7089142226
 
< 0.1%
2.9039857376
 
< 0.1%
Other values (15141)15998
98.5%
ValueCountFrequency (%)
0181
1.1%
0.01238723961
 
< 0.1%
0.014663829381
 
< 0.1%
0.020203008211
 
< 0.1%
0.025170260531
 
< 0.1%
0.027960116071
 
< 0.1%
0.028709331151
 
< 0.1%
0.029409832671
 
< 0.1%
0.036566967421
 
< 0.1%
0.036657853641
 
< 0.1%
ValueCountFrequency (%)
10.718247871
< 0.1%
10.472988371
< 0.1%
10.211971611
< 0.1%
10.098503051
< 0.1%
9.8032107041
< 0.1%
9.5500194651
< 0.1%
9.4705228861
< 0.1%
9.361694341
< 0.1%
9.3572484351
< 0.1%
9.3392534231
< 0.1%

LNCAP_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14848
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.52366053
Minimum0
Maximum10.01901215
Zeros318
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size127.1 KiB
2021-09-02T22:25:51.993471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3398317244
Q12.015348444
median3.600576441
Q34.926999833
95-th percentile6.68293842
Maximum10.01901215
Range10.01901215
Interquartile range (IQR)2.911651389

Descriptive statistics

Standard deviation1.930274248
Coefficient of variation (CV)0.547803692
Kurtosis-0.6766104237
Mean3.52366053
Median Absolute Deviation (MAD)1.444887145
Skewness0.07047483539
Sum57248.91263
Variance3.725958672
MonotonicityNot monotonic
2021-09-02T22:25:52.144940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0318
 
2.0%
3.362578312
 
0.1%
3.6415611911
 
0.1%
2.82861501711
 
0.1%
2.022357959
 
0.1%
2.7034902818
 
< 0.1%
2.2589872568
 
< 0.1%
4.722544748
 
< 0.1%
3.491769568
 
< 0.1%
1.7305786778
 
< 0.1%
Other values (14838)15846
97.5%
ValueCountFrequency (%)
0318
2.0%
0.0083357247791
 
< 0.1%
0.010298239991
 
< 0.1%
0.014935040041
 
< 0.1%
0.016885519261
 
< 0.1%
0.019254232911
 
< 0.1%
0.019459302381
 
< 0.1%
0.02142717581
 
< 0.1%
0.023217121291
 
< 0.1%
0.024337598261
 
< 0.1%
ValueCountFrequency (%)
10.019012151
< 0.1%
9.9131387351
< 0.1%
9.6866147711
< 0.1%
9.6594386321
< 0.1%
9.5885666551
< 0.1%
9.5090568551
< 0.1%
9.4070791321
< 0.1%
9.3897831541
< 0.1%
9.283618421
< 0.1%
9.2759971831
< 0.1%

LNCAP_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15647
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.203438231
Minimum0
Maximum10.13682209
Zeros349
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size127.1 KiB
2021-09-02T22:25:52.297904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07631100492
Q10.6781101745
median1.956277779
Q33.296716518
95-th percentile5.506760363
Maximum10.13682209
Range10.13682209
Interquartile range (IQR)2.618606344

Descriptive statistics

Standard deviation1.751905064
Coefficient of variation (CV)0.7950779101
Kurtosis0.237234816
Mean2.203438231
Median Absolute Deviation (MAD)1.303572909
Skewness0.8125895476
Sum35799.26094
Variance3.069171353
MonotonicityNot monotonic
2021-09-02T22:25:52.453315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0349
 
2.1%
1.5890645854
 
< 0.1%
0.4156187334
 
< 0.1%
2.2359485324
 
< 0.1%
0.33398711963
 
< 0.1%
0.7387052513
 
< 0.1%
0.8864595443
 
< 0.1%
2.8092012373
 
< 0.1%
0.96494819373
 
< 0.1%
0.98697000523
 
< 0.1%
Other values (15637)15868
97.7%
ValueCountFrequency (%)
0349
2.1%
0.0064330522181
 
< 0.1%
0.0073171447531
 
< 0.1%
0.007404218641
 
< 0.1%
0.0078954896971
 
< 0.1%
0.0083742498861
 
< 0.1%
0.0089154992221
 
< 0.1%
0.0089420794641
 
< 0.1%
0.0089523448841
 
< 0.1%
0.009265048821
 
< 0.1%
ValueCountFrequency (%)
10.136822091
< 0.1%
9.5851859131
< 0.1%
9.3816768131
< 0.1%
9.3399748561
< 0.1%
9.33072731
< 0.1%
9.2553340171
< 0.1%
9.2188566041
< 0.1%
9.0985591521
< 0.1%
9.0405700081
< 0.1%
8.9050340181
< 0.1%

LNCAP_3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14750
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.170622421
Minimum0
Maximum11.27863947
Zeros369
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size127.1 KiB
2021-09-02T22:25:52.615077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2118453749
Q11.553131411
median2.921058785
Q34.594828442
95-th percentile6.800958263
Maximum11.27863947
Range11.27863947
Interquartile range (IQR)3.041697031

Descriptive statistics

Standard deviation2.038556106
Coefficient of variation (CV)0.6429513942
Kurtosis-0.5720665357
Mean3.170622421
Median Absolute Deviation (MAD)1.506134227
Skewness0.4348772092
Sum51513.10248
Variance4.155710999
MonotonicityNot monotonic
2021-09-02T22:25:52.784593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0369
 
2.3%
4.97203195313
 
0.1%
2.5605798310
 
0.1%
1.8418412199
 
0.1%
2.243957489
 
0.1%
3.2143311188
 
< 0.1%
1.8496747078
 
< 0.1%
1.7539412967
 
< 0.1%
1.613093887
 
< 0.1%
1.3233947057
 
< 0.1%
Other values (14740)15800
97.2%
ValueCountFrequency (%)
0369
2.3%
0.01060722261
 
< 0.1%
0.011107889011
 
< 0.1%
0.011374117451
 
< 0.1%
0.013684049151
 
< 0.1%
0.013846665981
 
< 0.1%
0.014024164291
 
< 0.1%
0.014185180921
 
< 0.1%
0.016008974191
 
< 0.1%
0.016720586341
 
< 0.1%
ValueCountFrequency (%)
11.278639471
< 0.1%
10.859180461
< 0.1%
10.146056021
< 0.1%
10.077956671
< 0.1%
10.008548911
< 0.1%
9.9999685631
< 0.1%
9.862970651
< 0.1%
9.8622458921
< 0.1%
9.8620996691
< 0.1%
9.3509612321
< 0.1%

MR49F_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14935
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.335691402
Minimum0
Maximum11.18376443
Zeros347
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size127.1 KiB
2021-09-02T22:25:52.947006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2673336802
Q11.742734009
median3.415217881
Q34.790194171
95-th percentile6.372388118
Maximum11.18376443
Range11.18376443
Interquartile range (IQR)3.047460162

Descriptive statistics

Standard deviation1.916229079
Coefficient of variation (CV)0.5744623371
Kurtosis-0.7302829836
Mean3.335691402
Median Absolute Deviation (MAD)1.496568527
Skewness0.08503429769
Sum54194.97821
Variance3.671933882
MonotonicityNot monotonic
2021-09-02T22:25:53.099021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0347
 
2.1%
4.68066117410
 
0.1%
5.0830305348
 
< 0.1%
1.3782156417
 
< 0.1%
1.4212735617
 
< 0.1%
2.4582319466
 
< 0.1%
3.7151685136
 
< 0.1%
1.2320389946
 
< 0.1%
2.3714439636
 
< 0.1%
1.0674247285
 
< 0.1%
Other values (14925)15839
97.5%
ValueCountFrequency (%)
0347
2.1%
0.011168488721
 
< 0.1%
0.014740215851
 
< 0.1%
0.017295894181
 
< 0.1%
0.018467868361
 
< 0.1%
0.019222122041
 
< 0.1%
0.02115572991
 
< 0.1%
0.021350205391
 
< 0.1%
0.023740099291
 
< 0.1%
0.024033379551
 
< 0.1%
ValueCountFrequency (%)
11.183764431
< 0.1%
11.12360781
< 0.1%
10.496767891
< 0.1%
10.457572361
< 0.1%
10.263706941
< 0.1%
9.9093339351
< 0.1%
9.873552911
< 0.1%
9.475366191
< 0.1%
9.4042463341
< 0.1%
9.362017051
< 0.1%

MR49F_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15162
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.205655746
Minimum0
Maximum10.60045403
Zeros391
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size127.1 KiB
2021-09-02T22:25:53.254005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.100310536
Q11.230356571
median3.307936456
Q34.824569729
95-th percentile6.670607537
Maximum10.60045403
Range10.60045403
Interquartile range (IQR)3.594213158

Descriptive statistics

Standard deviation2.128807721
Coefficient of variation (CV)0.6640787063
Kurtosis-0.9330491874
Mean3.205655746
Median Absolute Deviation (MAD)1.732826412
Skewness0.1348126163
Sum52082.2889
Variance4.531822312
MonotonicityNot monotonic
2021-09-02T22:25:53.421933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0391
 
2.4%
4.0820159539
 
0.1%
3.6849521629
 
0.1%
2.2325430687
 
< 0.1%
3.8630818837
 
< 0.1%
3.0165935656
 
< 0.1%
3.4056014136
 
< 0.1%
2.8705710526
 
< 0.1%
2.6353174026
 
< 0.1%
3.1907203475
 
< 0.1%
Other values (15152)15795
97.2%
ValueCountFrequency (%)
0391
2.4%
0.0066224324431
 
< 0.1%
0.009704196681
 
< 0.1%
0.0097865110871
 
< 0.1%
0.01144787211
 
< 0.1%
0.011576384351
 
< 0.1%
0.011905177021
 
< 0.1%
0.012199315711
 
< 0.1%
0.014434268531
 
< 0.1%
0.014571445811
 
< 0.1%
ValueCountFrequency (%)
10.600454031
< 0.1%
10.090905991
< 0.1%
10.072499191
< 0.1%
9.9329871681
< 0.1%
9.8144462031
< 0.1%
9.5706777551
< 0.1%
9.3556303471
< 0.1%
9.3264710671
< 0.1%
9.2211234041
< 0.1%
9.1900040071
< 0.1%

MR49F_3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15462
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.015268981
Minimum0
Maximum9.446667343
Zeros406
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size127.1 KiB
2021-09-02T22:25:53.580385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06225739323
Q10.7613259992
median1.944723548
Q33.000945426
95-th percentile4.516097249
Maximum9.446667343
Range9.446667343
Interquartile range (IQR)2.239619426

Descriptive statistics

Standard deviation1.434541045
Coefficient of variation (CV)0.7118360172
Kurtosis-0.1312599355
Mean2.015268981
Median Absolute Deviation (MAD)1.1225988
Skewness0.5287846222
Sum32742.07514
Variance2.05790801
MonotonicityNot monotonic
2021-09-02T22:25:53.729641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0406
 
2.5%
1.6483888577
 
< 0.1%
2.9815697937
 
< 0.1%
3.1554032346
 
< 0.1%
1.8726500856
 
< 0.1%
2.7106508195
 
< 0.1%
2.8093397775
 
< 0.1%
2.2404112185
 
< 0.1%
1.8685133245
 
< 0.1%
2.654173435
 
< 0.1%
Other values (15452)15790
97.2%
ValueCountFrequency (%)
0406
2.5%
0.0062695025461
 
< 0.1%
0.006282127091
 
< 0.1%
0.0067043847581
 
< 0.1%
0.0068541345711
 
< 0.1%
0.0072557554991
 
< 0.1%
0.0073029350691
 
< 0.1%
0.0080188368721
 
< 0.1%
0.0081853967261
 
< 0.1%
0.0086486854371
 
< 0.1%
ValueCountFrequency (%)
9.4466673431
< 0.1%
8.6424187981
< 0.1%
8.4937134411
< 0.1%
8.147582281
< 0.1%
8.0374265171
< 0.1%
7.9183110751
< 0.1%
7.8969957711
< 0.1%
7.8408500221
< 0.1%
7.8363414321
< 0.1%
7.6702309971
< 0.1%

Interactions

2021-09-02T22:25:30.789973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:30.934272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:31.054826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:31.194878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:31.332796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:31.447865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:31.568712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:31.687867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:31.806895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:31.925156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:32.170866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:32.301058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:32.420334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:32.543899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:32.665058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:32.780961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:32.903330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:33.017877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:33.137322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:33.258145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:33.376721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:33.502391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:33.643609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:33.767797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:33.884417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:33.999975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:34.116220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:34.234132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:34.350042image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:34.465952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:34.585387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:34.706888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:34.843520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:34.958956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:35.072253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:35.192839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:35.304549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:35.426441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:35.547476image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:35.810844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:35.931570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:36.044132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:36.159290image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:36.280804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:36.403262image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:36.517329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:36.637616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:36.752960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:36.871220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:36.990316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:37.104350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:37.220621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:37.336402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:37.458141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:37.579161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:37.699673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:37.820191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:37.959605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:38.079450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:38.196870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:38.310184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:38.433581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:38.560095image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:38.682399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:38.798302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:38.917318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:39.037223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:39.153981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:39.269574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:39.391366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:39.513653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:39.642617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:39.776573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:39.902107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:40.217909image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:40.331969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:40.473966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:40.597104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:40.717765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:40.835081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:40.956758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:41.078375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:41.199993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:41.317352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:41.433636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:41.556846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:41.682884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:41.796941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:41.912565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:42.026432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:42.149275image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:42.266344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:42.386140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:42.504343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:42.623921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:42.741402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:42.855006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:42.972891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:43.089700image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:43.210697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:43.332575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:43.458007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:43.581444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:43.698936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:43.818391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:43.953198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:44.080967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:44.204744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:44.335261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:44.461590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:44.592515image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:44.721125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:44.859336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:44.981526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:45.095761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:45.211923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:45.333638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:45.681341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:45.793953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:45.914875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:46.028713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:46.147156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:46.262170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:46.377921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:46.499025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:46.617860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:46.732956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:46.847776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:46.967159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:47.082167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:47.198740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:47.314560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:47.439917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:47.560379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:47.674940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:47.790145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:47.905283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:48.018615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:48.133109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:48.247380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:48.371444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:48.484461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:48.602320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:25:48.737921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-09-02T22:25:53.878276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-02T22:25:54.061365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-02T22:25:54.241800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-02T22:25:54.428872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-09-02T22:25:48.980456image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-02T22:25:49.262858image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

geneC42_1C42_2C42_3C42B_1C42B_2C42B_3LNCAP_1LNCAP_2LNCAP_3MR49F_1MR49F_2MR49F_3
0ENSG000000000032.0203083.1129674.4628645.8024583.6139095.1969593.4637636.0385253.0206163.0906184.8566302.818450
1ENSG000000004194.2233501.7405243.5830937.1499232.6332725.0468615.3233557.2869432.0321272.7553595.0891893.712340
2ENSG000000004572.8699192.5592743.9796991.5960581.1567472.9121013.6777164.1296011.9652404.1788992.3053541.173922
3ENSG000000004601.7519042.2881794.5072093.4217402.3498311.4075283.0525855.8594532.0441423.0708653.1975951.739958
4ENSG000000010363.4811973.8734326.3494525.6836033.5543723.4157895.2415174.5483652.8612954.8721324.5044073.250109
5ENSG000000010842.9423302.5686413.5327857.2686473.9320475.2336055.0190591.9635372.2120934.3329835.0592494.626412
6ENSG000000011672.2360611.2069654.8333173.3469004.2459985.4387455.5227574.4879463.5527542.3201593.7548143.025724
7ENSG000000014601.1858081.4072072.0298622.2419340.4173792.1145802.6654554.0188951.0312361.9402691.6142351.027777
8ENSG000000014612.1761792.6037973.8268456.8071532.4246234.9236544.6109183.5720042.6357683.8935724.7635682.041859
9ENSG000000014973.1808362.9726554.4550145.3529772.6037863.0396766.1198914.0875312.4195994.0225505.3234473.182143

Last rows

geneC42_1C42_2C42_3C42B_1C42B_2C42B_3LNCAP_1LNCAP_2LNCAP_3MR49F_1MR49F_2MR49F_3
16237ENSG000002834860.2712551.9255602.8854540.3050870.3823660.8699750.0000000.1758300.2222680.1314160.0825741.648389
16238ENSG000002834910.4712551.6090233.0470160.4328202.6829260.8478273.9918510.6849491.7564521.5000620.4940982.121968
16239ENSG000002834980.6301581.3695923.4717390.3196020.6504472.1511933.9705160.3503261.2677281.3719250.5684153.412563
16240ENSG000002835110.9764710.6334611.7484750.8908371.2923881.7353630.0372960.0116840.3899711.1579520.1511812.265861
16241ENSG000002835262.0083671.5131952.9909821.3961512.4742433.4464765.2180360.5384444.4535361.4385130.7255424.200088
16242ENSG000002835770.0000000.0000000.0000000.6685171.0139590.6834600.0000000.0000000.0000000.0898110.0233671.100471
16243ENSG000002836330.0000000.0000000.0000000.3785882.3128892.6725580.0000000.0000000.0000000.0000000.0000000.000000
16244ENSG000002836670.0802860.8915432.1114690.2020830.6456390.6906462.2589870.1627130.1692811.8890440.5253943.102266
16245ENSG000002836740.5030643.5600784.1922341.2053952.1487922.6781592.1817620.8319791.4238401.2864980.4031383.155403
16246ENSG000002836890.1101530.2270551.2767670.2915911.5514550.8100001.2239370.1832020.9204411.1359270.3755982.738001